Publications: Peer-reviewed journal articles (by staff)
A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA
Wood SA, Pochon X, Laroche O, von Ammon U, Adamson J, Zaiko A (2019). A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species-specific detection in environmental DNA. Molecular Ecology Resources, in press
DOI link here
Targeted species‐specific and community‐wide molecular diagnostics tools are being used with increasing frequency to detect invasive or rare species. Few studies have compared the sensitivity and specificity of these approaches. In the present study environmental DNA from 90 filtered seawater and 120 biofouling samples was analyzed with quantitative PCR (qPCR), droplet digital PCR (ddPCR) and metabarcoding targeting the cytochrome c oxidase I (COI) and 18S rRNA genes for the Mediterranean fanworm Sabella spallanzanii. The qPCR analyses detected S. spallanzanii in 53% of water and 85% of biofouling samples. Using ddPCR S. spallanzanii was detected in 61% of water of water and 95% of biofouling samples. There were strong relationships between COI copy numbers determined via qPCR and ddPCR (water R2 = 0.81, p < .001, biofouling R2 = 0.68, p < .001); however, qPCR copy numbers were on average 125‐fold lower than those measured using ddPCR. Using metabarcoding there was higher detection in water samples when targeting the COI (40%) compared to 18S rRNA (5.4%). The difference was less pronounced in biofouling samples (25% COI, 29% 18S rRNA). Occupancy modelling showed that although the occupancy estimate was higher for biofouling samples (ψ = 1.0), higher probabilities of detection were derived for water samples. Detection probabilities of ddPCR (1.0) and qPCR (0.93) were nearly double metabarcoding (0.57 to 0.27 marker dependent). Studies that aim to detect specific invasive or rare species in environmental samples should consider using targeted approaches until a detailed understanding of how community and matrix complexity, and primer biases affect metabarcoding data.